基于机器学习的人工智能技术专利数据集构建新策略

颠覆性技术是一个具有复杂的内在结构的技术群。从空间维度来看,颠覆性技术是包含了主导技术、辅助技术、支撑技术的复杂技术群,涉及多学科、多领域。在此背景下,运用科学计量的方法对颠覆性技术进行科技评价和科学技术演变规律探索面临挑战,实质表现为数据检索。本文探索了一种基于机器学习的专利数据集构建新策略,将专利检索任务作为机器学习的二分类任务,类似于信息检索中基于主动学习的查询分类思想,并提出了将F-measure特征最大化方法与CNN (convolutional neural networks)模型相结合的文本分类改进方法。本文以人工智能(artificial intelligence,AI)技术域为例进行训练实验,实验结果的准确率、召回率和F1值分别达到98.01%、97.04%和97.89%,这表明本文提出的策略能够精准地识别人工智能专利,提高了专利检索的准确率和召回率,以利于构建精、准、全的人工智能技术域专利数据集。     

Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy

The growing Artificial Intelligence (AI) age has been flooded with several innovations in algorithmic machine learning that may bring significant impacts to industries such as healthcare, agriculture, education, manufacturing, retail etc. But challenges such as data quality, privacy and lack of a skilled workforce limit the scope of AI implementation in emerging economies, particularly in the Public Manufacturing Sector (PMS). Therefore, to enhance the body of relevant literature, this study examines the existing challenges of AI implementation in PMS of India and explores the inter-relationships among them. The study has utilized the DEMATEL method for identification of the cause-and-effect group factors. The findings reveal that poor data quality, managers' lack of understanding of cognitive technologies, data privacy, problems in integrating cognitive projects and expensive technologies are the main challenges for AI implementation in PMS of India. Moreover, a model is proposed for industrial decision-makers and managers to take appropriate decisions to develop intelligent AI enabled systems for manufacturing organizations in emerging economies.     

<Emphasis Type="Italic">EveTAR</Emphasis>: building a large-scale multi-task test collection over Arabic tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR, the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets.     

Increasing cheat robustness of crowdsourcing tasks

Crowdsourcing successfully strives to become a widely used means of collecting large-scale scientific corpora. Many research fields, including Information Retrieval, rely on this novel way of data acquisition. However, it seems to be undermined by a significant share of workers that are primarily interested in producing quick generic answers rather than correct ones in order to optimise their time-efficiency and, in turn, earn more money. Recently, we have seen numerous sophisticated schemes of identifying such workers. Those, however, often require additional resources or introduce artificial limitations to the task. In this work, we take a different approach by investigating means of a priori making crowdsourced tasks more resistant against cheaters.     

2000年以来我国图书馆学方法论研究述评

论文从方法论体系、一般研究方法、哲学方法、图书馆学专门方法以及移植法等方面对2000年以来我国图书馆学方法论的研究现状进行述评,并就其今后的研究重点和研究方法提出了一些建议.     

图书馆学方法论研究中的解释学问题

解释学对方法论的理解,与传统科学对方法论的理解有很大的不同。当前图书馆学哲学方法研究中存在的问题,是研究者仍然保持传统科学方法的旧观念,在解读新的哲学理论中,不能改变自己的"前理解",达到"视界融合",其结果是出现"误读",难以获得新的哲学方法。     

21世纪我国图书情报学方法论研究综述

按照王崇德关于图书情报学方法论的分类方法,从哲学方法、一般方法和专门方法三个方面对我国图书情报学近10年来的新发展做了系统的梳理,指出我国图书情报学研究方法存在欠缺实证研究、缺乏针对网络环境的新方法等问题,提出要加强研究方法的实证研究和规范化发展,建立适应信息化环境的研究方法,建立一个稳定、统一、规范的方法论体系等解决措施。     

任务驱动下的学术数据库新手心智模型演进及学习模式研究

[目的/意义]如何理解新手在与学术数据库交互过程中其心智模型的演进过程和学习模式是值得关注的问题,探究任务类型对学术数据库新手用户心智模型和学习模式的影响能够更好地帮助用户使用学术数据库。[研究设计/方法]本研究通过77位新手用户参与完成三种不同类型的任务(事实型搜索、探索型搜索和干涉型搜索)前后分别绘制的中国知网概念图的实验,对概念图的结构差异和内容差异进行分析,探讨不同任务情境下学术数据库新手心智模型的演进规律及学习模式。[结论/发现]研究表明用户信息搜索前后心智模型的演进模式受到任务类型的驱动,呈现出不同的概念图结构和维度内容;用户在不同任务的搜索前后均进行了学习行为,但学习模式存在差异,大多数新手用户在事实型任务和干涉型任务搜索后进行了有意义的学习,而在探索型任务搜索后用户进行了初步学习。[创新/价值]从任务类型的影响视角丰富了“搜索即学习”中的新手用户心智模型和学习模式的研究内容,有利于信息专业人员为新手开展信息检索技能培训和优化检索系统设计。     

Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong

Despite the exponential growth in the popularity of artificial intelligence (AI), our knowledge on the public perception of AI, especially in the context of local government services, is still limited. To bridge this gap, this study aims to provide empirical evidence and insights into public perceptions concerning the use of AI in local government services. Our methodological approach involves collecting data via an online survey from the residents of three major Australian cities—i.e., Sydney, Melbourne, Brisbane—and Hong Kong (n = 850), and performing statistical analyses. We found that: (a) Ease of using AI is significantly and positively influenced by attitude towards AI; (b) Attitude towards AI significantly and positively influences perceived usefulness of AI in local government services; (c) AI is seen useful in resource management and to improve delivery of service, reduction of cost to provide urban-service, improvement of public safety, and monitoring the effectiveness of strategies to manage environmental crisis, and; (d) AI is more positively perceived by Australians in comparison to Hong Kongers, indicating the impact of contextual and cultural differences. The research findings inform local government authorities—e.g., urban policymakers, managers, and planners—on their AI policy, planning and implementation decisions.     

Learning-based summarisation of XML documents

Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper, we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree. We view these results as a step towards the intelligent summarisation of XML documents.

基于学科论的图书馆学结构与学科体系研究

图书馆学的学科论是对图书馆学进行研究探讨的理论。其主要任务是研究图书馆学的结构与学科体系。首先,在"信息基本循环"过程中认识图书馆学的对象、图书馆学研究者、图书馆学及其关系。其次,分析图书馆学以学科对象为核心,以学科核心价值观为轴,以现象学、元学、方法学研究为层次结构,以理论、方法、应用研究为三维坐标;以共时结构为经,以历时结构为纬,以"双律性"为依据,而不断发展的、开放的、有序化的、具有解释功能的结构体系。再次,从著作体系与教材体系方面,探讨图书馆学科体系的建设。     

近四年来我国图书情报学方法论研究综述（2005～2008）

论文从图书情报学方法论理论研究、哲学方法、一般方法、专门方法以及移植方法等方面对2005—2008年我国图书情报学方法论的研究现状进行了回顾与总结。参考文献24。     

基于德尔菲法的我国情报学哲学理论实证研究

采用德尔菲法收集当前我国情报学界23名具有教授职称的专家关于情报学哲学理论的观点。研究表明：①专家对情报学哲学理论的认知一致性较低,存在12类不同的观点。②大多数专家认为情报学的哲学基础应该不仅仅由一种哲学理论构成,而是多种哲学理论并存,分歧在于应该由哪些哲学理论并存。③波普尔的"三个世界"理论、解释学、信息哲学能够用于指导情报学研究的多个维度;马克思主义哲学对情报学研究的指导集中在宏观层面;经验主义、理性主义、实证主义、实用主义和建构主义对完善情报学的方法论有指导意义;人本主义和认知理论可用于指导情报用户研究。该研究能够为构建我国情报学哲学理论框架提供指导。     

专利检索系统中可视化查询的比较研究

专利检索是一个非常复杂的过程,用户为了迅速高效地完成检索任务需要得到支持。专利检索过程的许多环节可以借助一些工具完成,其中就包括查询(式)构造工具。查询构造是一项高度依赖人工的任务,工具只能实现对可能有用数据进行预先计算,并针对用户进行可视化。信息检索系统中,查询过程和查询结果可视化的方式有很多。本研究提出了两种典型的原型系统设计,用于在专利检索过程中对不同的查询表达式进行比较。原型包含查询表达式构造因素和结果集大小因素,两种因素对于专利领域专家探究查询表达式的调整对检索效率的影响至关重要。本文开发的系统有助于在专利检索过程中对复杂查询表达式进行逐步优化,系统设计思想基于了领域专家型知识工程。     

人工智能时代情报学发展走向之辨——对本体论、感知论、方法论、服务论的再思考

人工智能相关技术的迅猛发展和逐步普及,使得情报学难以避免人工智能的融合趋势,无论这种融合是主动为之或是被动选择。然而学界对人工智能时代情报学学科的发展走向并未予以充分探讨,大部分研究均将焦点置于大数据与下一代互联网环境中进行论述。本研究将人工智能时代情报学学科走向解构为"本体论""感知论""方法论""服务论"四个论题,对每个论题借由理论面向与实践面向的二分法展开阐释,并从整体视角上提出应在人工智能洪流中找准情报学定位,不能忽视"人"在情报学中的价值以及重视跨学科融合、跨领域应用的发展趋势。     

Evaluating the Children's Literature Collection

This article reports on an evaluation study of the Children's Literature Collection conducted at Gustavus Adolphus College Library in 1992. Among the goals were to assess quality and extent of the collection and to find out why students were dissatisfied with the collection. The methodology included the inductive method of checking a collection against a major review instrument and annual lists of "best" titles, as well as a user survey in order to compare findings about quality and size of the collection with students' perception of the two measures. Surprisingly it was found that students major dissatisfaction stemmed not so much from lack of quality and quantity of the collection but rather from problems with access via the online catalog and shelf arrangement.     

信息检索实验中的任务设计——真实与模拟仿真工作任务的比较研究

在信息检索系统评估和信息搜寻行为研究中,实验法是常用的研究方法,而实验中的任务设计是实验成功的关键环节。设计模拟仿真工作任务情境是近年来常用的任务设计方式,而它能否替代用户的真实工作任务在很大程度上影响着实验研究结果的科学性。为验证此方法,招募42位本科生参加实验,要求其为完成一项真实工作任务和一项仿真工作任务,在CNKI中展开搜索。通过分析实验参加者在实施两项搜索任务中的交互信息检索行为和检索绩效,发现仿真工作任务与真实工作任务在属性上尽管存在某些显著差异,但并未对用户交互信息搜索行为及绩效产生显著影响。因而,仿真工作任务可以替代真实工作任务用于交互信息检索实验研究,但需要进行合理设计。     

Is the archivist a “radical atheist” now? Deconstruction,its <Emphasis Type="Italic">new wave</Emphasis>, and archival activism

Hägglund’s “radical atheism”—innovative thinking within the philosophical current of “speculative materialism”—revitalizes deconstruction and provides an important basis to define parameters for the archivist’s role as activist for social justice. This paper argues postmodern archival theory gets deconstruction wrong by misreading Derrida’s “Archive fever” as a theory of “archontic power”; this misleads archivists on the call for justice. Properly understanding that justice is undecidable, radical atheism explodes the tension between postmodernists’ appreciation of all views and perspectives and their commitment to right unjust relations of power. This paper first advances the negative argument that “Archive fever” is not about power and injustice. It then advances the positive argument that “Archive fever” is Derrida’s effort to look at actual archives to resolve Freud’s problematic theorizing of a “death drive.” In a close and comprehensive reading of “Archive fever,” this paper explores the notion of “archive fever” as a death drive and suggests Derrida’s efforts are inconclusive. Viewed through the lens of radical atheism, the archive’s “traces”—the material of actual archives writ large in the manner of Derrida’s thinking about a universal archive—serve to mark the flow of time. Understanding the structure of the trace reveals the source of internal contradictions, discontinuities, and instabilities in the meaning of all things. It explains why justice is undecidable. In face of the unconditional condition of this undecidability, we as archivists and humans are compelled to make decisions and to act. Deconstruction politicizes our actions and evokes a responsibility that cannot be absolved.     

Detection As Multi-Topic Tracking

The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning all arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a perfect tracking system could solve the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.     

A Critique of the FRBR User Tasks and Their Modifications

The four FRBR user tasks have become widely accepted as functions of the library catalog, but there have been only sporadic discussions concerning their validity and sufficiency, despite their modification in the models subsequently presented in the FRAD, FRSAD, and draft FRBR-LRM reports. This article presents a critique of the four variant sets of user tasks, and proposes an extended set of six generic end-user tasks, applicable to both bibliographic and authority data: locate, collocate, connect, identify, select, and obtain. The article also outlines their interrelationships and suggests those tasks that may be particularly well supported by professional cataloging.